In this paper, we present a novel no-reference (NR) metric to assess the quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering the key human visual sensitivity factors such as, edge amplitude, edge length, background activity and background luminance. The extracted features with the subjective test results are used to train a multi-layer perceptron (MLP) neural network. Experimental results show that the prediction of the trained neural network is very close to the mean opinion score (MOS). The subjective test results of the proposed metric are compared with the Wang-Bovik's NR blockiness metric. Further, this metric can be extended to assess the quality of the MPLG/H.26x compressed videos.


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    Titel :

    An HVS-based no-reference perceptual quality assessment of JPEG coded images using neural networks


    Beteiligte:
    Babu, R.V. (Autor:in) / Perkis, A. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    217502 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Babu, R. V. / Perkis, A. | British Library Conference Proceedings | 2005


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    Local region-based image quality assessment independent of JPEG and JPEG2000 coded color images

    Sazzad, Z.M.P. / Horita, Y. | British Library Online Contents | 2008



    Enlargement method for JPEG-coded images with the prediction of high-frequency components [5014-05]

    Takahashi, Y. / Taguchi, A. / Society for Imaging Science and Technology | British Library Conference Proceedings | 2003